1. SIMO Python/XML Simulator Current situation 28/10/2005 SIMO Seminar 28.10.2005 Antti Mäkinen Dept. of Forest Resource Management / University of Helsinki

2. What can be calculated at the moment? Development of different variables at stand level...

3. What can be calculated at the moment? Development of different variables at stand level...

4. What can be calculated at the moment? Development of different variables at stand level...

5. What can be calculated at the moment? Development of different variables at stand level...

6. What can be calculated at the moment? Development of different variables at stand level...

7. What can be calculated at the moment? Development of different variables at stand level...

8. What can be calculated at the moment? Diameter distributions and tree level attributes

9. Just for comparison with J simulator...

11. What can be calculated at the moment? Estimating forest variable development at both stand level & tree level is possible at the moment (300+ models implemented), but  Forestry operations not yet implemented in the simulator → ”real world” simulations not yet possible  Bucking models still not ready  Optimizing module still missing

12. How the simulation process works in SIMO? XML Files SIMULATOR MODEL LIBRARY Reporter Module IN: data, simulation control, modelchains, model definitions OUT: results IN: modelname, input variables OUT: model result, warnings & errors IN: XML data OUT: transformed XML, graphs SIMULATION PROCESS

13. What is missing? XML Files SIMULATOR MODEL LIBRARY Reporter Module Optimizer Module MODEL LIBRARY Validator Module

14. XML Files  Data XML Data XML  Simulation control XML Simulation control XML  Model Chain XML Model Chain XML  Model XML Model XML  Result XML Result XML

15. Model Library  Includes all models used in the simulator  Programmed with C language as a Dynamic Link Library (DLL)  Models are C functions that are called from the simulator (model definitions also in the Model.xml)  Users can add new models to the library or create additional model libraries  Reports warnings and errors to the simulator  Risk level models not yet implemented

16. SIMULATOR  1. version of simulator programmed with C/C++  Later the programming language was changed to Python, because of:  Simple and concise syntax → easier readability of code and possibility of developing the simulator faster http://www.python.org  Good combatibility with C language  Number of useful readymade open source tools for variety of purposes  Code documentation is underway

17. SIMULATOR  Intakes simulation control instructions, model chains, model definitions and data in XML format  Processes the user defined model chains for each computing unit in the data  Calls the model library whenever some value needs to be calculated (Python/C interface ctypes)  Prints the resulting values into a result XML file  Transforms the XML data from different files to simulators own data structure (more efficient than ElementTree data structure)

18. Reporting Module  Used for visualizing data & transforming the results from XML format to other formats  Intakes data and processing instructions in XML format  At the moment can plot different kinds of graphs of given variables (matplotlib) toolset  XML transformations to be implemented later...

19. Missing modules  Optimizer module Finds the best alternative from the alternatives generated by the simulator Possibly many alternative optimizing methods?  Validator module Validates the XML files with XSD (Schema) files and by external rules Makes sure that the XML files are well-formed and contain all necessary elements

20. Strengths of SIMO XML Simulator  Virtually any kind of model can be used in the simulations and added to the model library  User can define the model chains freely for different kinds of simulations  User can define correction/rectification factors for the models, (eg. different factors for geographical areas)  Extensive warning and error reporting system (risk control coming later...)  Data levels are not confined to strict predifined standard

21. Model risk management –individual variables Minimum and maximum limits of individual variables have been defined Documented in ModelXMLModelXML Limits have been coded into ModelLibrary -> throws warnings if the Individual parameter values are out of bounds How the minimum and maximum limits are defined? Limits defined by author (caused by data, model shape, …) Limits of modeling data Model is tested with those limits using NFI-data as test data. Does the model function properly if the Individual parameter values are out of bounds? For example: Basal area growth model (Vuokila & Väliaho) for Scots pine on mineral soilsBasal area growth model

22. Model risk management –interaction Interaction between variables age ba Accepted combinations of varibles (120, 5) not accepted (20, 32) not accepted Solution alternatives: Logit-model: propability that the estimate is in acceptable area (at least linear regression was not flexible enough) Grid: area of combinations of variables is divided into cells. Every cell has information is the estimate acceptable or not

23. Model risk management Two levels 1. Individual parameter values out of bounds 2. All individual parameter values acceptable, but is the specific combination of them acceptable? Case 1: already in the simulator Case 2: Suggestion 1. get the k nearest neighbours from the VMI data, 2. evaluate the model for the data point and the k nearest neighbours. 3. If the difference for the model estimate between the data point and the neighbours is too big, generate an event of ”unacceptable” model estimate

24. Isn’t that procedure too heavy computationally? Probably, not yet evaluated But what about if we store the risk evaluation results and use those primarily: 1. Is it safe to call ModelA with parameters (5, 6, 10) when we accept risk level X? 2. Has the risk been evaluated with parameter values (5,6,10) and risk level X before. If yes, get the answer from a table of risk evaluations 3. If not, get k nearest neighbours for data point (5,6,10), evaluate the model with (5,6,10) and k neighbours 4. Store the risk evaluation result and the mean model result for k neighbours for the data point (5,6,10) and risk level X

26. Open questions: When evaluating model result shall we compare it to: values derived directly from the nearest VMI permanent sample plots OR model estimates for the nearest VMI sample plots?

27. Software license for SIMO Types of Open Source licenses MIT & Co: “Do whatever you want” LGPL: “Everything you do to the original code must be open source, anything on top of that can be closed” GPL & Co: “Everything you do is open source, …well almost” GPL under the hood: "derivative work" or "mere aggregation“? Derivative work must be open source, but aggregation can be closed source

28. The case of MySQL Double licensing: open source GPL, commercial development with a commercial license that allows closed source

29. General software architecture Individual components that communicate over the network Validator Simulator – this is well underway Optimiser Reporter – simulation results to figures and other data formats than XML, or different XML format etc. Implications to licensing? What about if one of the components uses a sub component that is published under GPL?

30. Architecture continued TCP/IP based communication Security issues? secured traffic (SSL, SSH) inside firewall Scalable